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J Am Diet Assoc. Author manuscript; available in PMC Oct 1, 2012.
Published in final edited form as:
PMCID: PMC3190578
NIHMSID: NIHMS326803
Computerized portion size estimation compared to multiple 24-hour dietary recalls for measurement of fat, fruit, and vegetable intake in overweight adults
Deborah J. Toobert, PhD, Senior Research Scientist, Lisa A. Strycker, MA, Senior Research Associate, Sarah E. Hampson, PhD, Senior Research Scientist, Erika Westling, PhD, Research Associate, Steven M. Christiansen, BA, President, Thomas G. Hurley, MS, Senior Biostatistician and Manager, and James R. Hébert, MSPH, ScD, Health Sciences Distinguished Professor
Deborah J. Toobert, Oregon Research Institute, 1715 Franklin Blvd., Eugene, OR 97403-1983, Phone: (541) 484-2123, Fax: (541) 434-1505;
Deborah J. Toobert: deborah/at/ori.org; Lisa A. Strycker: lisas/at/ori.org; Sarah E. Hampson: sarah/at/ori.org; Erika Westling: erikaw/at/ori.org; Steven M. Christiansen: steve/at/intervisionmedia.com; Thomas G. Hurley: thurley/at/mailbox.sc.edu; James R. Hébert: jhebert/at/mailbox.sc.edu
Corresponding author and author who will handle reader requests for reprints: Deborah J. Toobert, PhD, Senior Research Scientist, Oregon Research Institute, 1715 Franklin Blvd., Eugene, OR 97403-1983, Phone: (541) 484-2123, Fax: (541) 434-1505, deborah/at/ori.org
Validated self-report methods of dietary assessment exist, and might be improved both in terms of accuracy and cost-efficiency with computer technology. The objectives of this preliminary study were to develop an initial version of an interactive CD-ROM program to estimate fruit, vegetable, and fat intake, and to compare it to multiple 24-hour dietary recalls (24HR; averaged over 3 days). In 2009, overweight male and female adults (N = 205) from Lane County, OR completed computerized and paper versions of fruit, vegetable, and fat screening instruments, and multiple 24HR. Summary scores from the ten-item NCI Fruit and Vegetable Scan (FVS) and the 18-item Block Fat Screener (BFS) were compared to multiple 24HR-derived fruit/vegetable and fat intake estimates (criterion measures). Measurement models were used to derive deattenuated correlations with multiple 24HR of paper and CD-ROM administrations of FVS fruit intake, FVS vegetable intake, FVS fruit and vegetable intake, and BFS fat intake. The computerized assessment and paper surveys were related to multiple 24HR-derived fruit/vegetable and fat intake. Deattenuated correlation coefficients ranged from 0.50 to 0.73 (all P ≤0.0001). The CD-ROM-derived estimate of fruit intake was more closely associated with the 24HR (r=0.73) than the paper-derived estimate (r=0.54; P<.05), but the other comparisons did not differ significantly. Findings from this preliminary study with overweight adults indicate the need for further enhancements to the CD-ROM assessment and more extensive validation studies.
Keywords: food portion estimation, fruit and vegetable intake, fat intake, overweight adults
Validated self-report methods of dietary assessment might be improved in terms of accuracy and cost-efficiency with computer technology (1,2). Accurate dietary measurement is necessary for developing and testing dietary interventions to reduce obesity and to address other diet-related medical concerns. Traditional dietary assessment techniques are subject to portion-size and other inaccuracies (36), and studies using biomarkers indicate that self-report instruments generally result in under-reporting of energy intake (714).
Given the known inaccuracies in serving-size estimation (15), various methods have been used to verify serving sizes, including weighed food records (16), special plates and bowls (1719), direct or covert observation (20,21), matching foods to models (22), two- or three-dimensional realistic or abstract aids (2327), portion-size photographs (28,29), food models plus photos (4), and portion-size photographs compared to self-served foods (30). Nelson, Atkinson, and Darbshire (2) showed that using multiple photographs was more accurate than a single, average-portion photograph. However, Beasley, Davis, and Riley (31) found that a Web-based dietary history questionnaire using food photographs had comparable reliability and validity as the paper version, but did not improve the relationship of the diet history questionnaire to other food intake measures (eg, 24HR, food records).
A few serving-size estimation trials have employed computer technology (30,3234). A Web-based serving-size estimation program by Riley et al (35) shifted under- to overestimation. An interactive serving-size assessment by Foster et al (36) produced estimates closer to the actual weight of the food compared to food models and photographs. A key advantage of computerized dietary assessment is that users may adjust the size of on-screen servings to match intake (2,3739). Other potential benefits are multilingual and low-literacy capacity (40), increased engagement (41), and accessibility for ethnically diverse subgroups (4246). Existing validations of standard dietary assessment methods indicate that <50% of the variability in the true criterion method is accounted for by standard test methods, including doubly labeled water (47,48). Reporting accuracy depends on estimating portion sizes of food consumed, as well as bias, forgetting, and other sources of inaccuracies. Therefore, opportunities exist for potentially improving the accuracy of brief screeners and other frequency methods with the use of interactive visual information. Since rates of overweight and obesity among adults are especially troublesome, there is a need for accurate dietary estimation and effective dietary interventions in this population.
The purpose of the present study was to develop a preliminary version of an interactive computer program (CD-ROM) to estimate fruit, vegetable, and fat intake, and then correlate these estimates with multiple 24-hour dietary recalls (24HR; averaged over 3 days) in a sample of overweight adults. The CD-ROM-based program was built on previous methods developed and/or tested by Block and colleagues (49), Thompson and colleagues (50,51), and Peterson and colleagues (52). The primary research question was: Can a computer program be designed to provide estimates of fruit/vegetable and fat intake that are more highly correlated with the multiple 24HR estimates than paper versions?
Sample
A total of 207 overweight or obese adults was recruited and enrolled in the study from June, 2007 through November, 2009. Participants were paid $50. The Oregon Research Institute Institutional Review Board approved the study protocol and all participants provided written informed consent. Power analyses indicated that with 200 individuals the study had sufficient power to detect medium to small effects (R2=0.05).
Interactive CD-ROM Program
Two widely used dietary screeners were adapted for the CD-ROM program: the National Cancer Institute’s (NCI) revised Fruit and Vegetable Scan (FVS) (5155) and the Block fat screener (BFS) (49). The FVS assesses frequency of eating nine food categories, includes four portion size options, and estimates daily servings of fruits and vegetables using the 1998 United States Dietary Assessment Food Guide Pyramid defined servings (56). The original BFS (49,55,57) consisted of foods from the Second National Health and Nutrition Examination Survey that primarily contribute to fat intake. Fried chicken, pizza, and ice cream were added because of their high fat content and popularity (58). A fourth portion size option was added to the BFS to parallel the FVS. Respondents reported food intake over the past 2 months.
The computer program featured life-size color photographs for each portion size of each food on the BFS and FVS. Photographs were taken at an angled perspective similar to that experienced by a person seated at the table. Foods were cooked and styled as they are most commonly prepared and served. Each food serving was shown on the same dining plate, with silverware, napkin, and placemat as size referents (18). Most foods were photographed in multiple forms (eg, mashed or baked potatoes). For each food, users first chose the form in which they wanted to view the food. Then, presented with a full-size on-screen image of the food, users could use a slider bar to view four different portion sizes before selecting their own serving. To reduce response bias, options were not labeled “small,” “medium,” “large,” and “super-size”; they were shown as four points, with measurement information below (eg, ½ cup, 1 cup, 2 cups, more than 2 cups).
Study Procedures
Participants completed an informed consent form and demographic and other psychosocial measures, and received materials for their first three telephone-administered 24HR. Two weeks later, they returned for a second visit, at which they completed the CD-ROM program and paper versions of the FVS and BFS (with order randomly assigned). Three more 24HR were conducted in the 14 days after this visit, and then participants returned for a third visit to repeat the CD-ROM and paper surveys (data not presented).
Measures
Demographic data and participant characteristics
Variables included height and weight (using a stadiometer and calibrated digital scale), education, employment status, income, marital status, race/ethnicity (categories defined by the investigator), health status, smoking status, living arrangement, whether the participant was currently on a diet to lose weight, where most meals were eaten, who did most of the cooking, who served the food, and how often the participant ate second helpings. The baseline survey included an 18-item version of the impression management scale of the Balanced Inventory of Desirability Responding (59) and two scales from the short Test of Functional Health Literacy in Adults (S-TOFLA) (60).
Dietary intake
Up to three 24HR recalls, including one weekend day, were obtained by telephone per participant. The interviews were conducted by registered dietitians at the Diet Assessment Research Unit of the University of South Carolina who were trained in the use of Nutrition Data System for Research (NDSR) software versions V2007 and V2008, developed by the Nutrition Coordinating Center, University of Minnesota, Minneapolis, MN. Final calculations were completed using NDSR version V2008. Participants were given a validated two-dimensional visual aid to help them estimate the amounts of foods they had eaten (24). NDSR software generates summary information about food intake that may be compared with BFS and FVS summary variables.
Statistical Analyses
Scores were calculated for multiple-item instruments using procedures previously established for each instrument. From the FVS, constructs were created to estimate total daily servings of fruits, vegetables, and fruits and vegetables combined (51). From the BFS, an estimate of daily grams of fat intake was computed (49). From the multiple 24HR averaged over 3 days, variables were created to reflect daily servings of fruits, vegetables, fruits and vegetables combined, and total fat grams, using the food group serving count system integrated into NDSR. The FVS variables were positively skewed and were square-root transformed for a more normal distribution.
Differences between the screener values and multiple 24HR (averaged over 3 days) values were calculated for each variable. The mean of multiple 24HR was calculated to estimate average intake for all outcome variables, as well as used to calculate the difference with screener estimates of intake. To estimate deattenuated correlations, a measurement error model (61) was specified, in which the multiple 24HR was the reference instrument for the CD-ROM program and paper surveys. The model assumes that the multiple 24HR is unbiased and contains only within-person error. Measurement error models were run separately for CD-ROM vs multiple 24HR and for paper vs multiple 24HR for fruit intake, vegetable intake, fruit and vegetable intake, and fat intake. Additional separate models were fit containing either age, sex, or order of administration as a covariate (these factors had minimal impact and are not discussed further). P≤0.05 was used for the measurement models to reject the null hypothesis.
All statistical analyses were conducted using SPSS for Unix (release 6.1, SPSS Inc., 217 Chicago, IL) and Statistical Analysis Software (version 9.2, 2009, SAS Institute Inc., Cary, NC).
Participant characteristics
Of people reached by telephone, 70% were deemed eligible, 97% agreed to participate, and 85% completed all three assessments. Of those not completing the study, most missed appointments and were thereafter unreachable (n=28). Baseline participant characteristics are presented in Table 1. The recruited sample averaged 59 years of age. Most were female, were self-identified as white, had less than a $50,000 income, and were not employed. Average body mass index was 32.2 kg/m2, and most participants had few comorbidities. Average health literacy and numeracy were moderate to high, and socially desirable responding scores were low.
Table 1
Table 1
Baseline characteristics of the recruited sample
Comparison of screener scores relative to multiple 24HR
The paper and computerized FVS versions produced similar estimates of fruit intake (mean=1.84 servings for paper vs 1.87 for CD-ROM; difference nonsignificant); see Table 2. The two FVS versions yielded significant, but small (0.3 servings per day) differences in estimates of vegetable intake (mean=3.68 for paper vs 3.97 for CD-ROM, paired t(194)=2.35, P=.02) and of fruit and vegetable intake combined (5.52 for paper vs 5.83 for CD-ROM, paired t(194)=2.27, P=.02). Fat intake estimates from the two BFS versions did not differ significantly. Computerized and paper screeners yielded higher estimates of fruit, vegetable, and fruit and vegetable servings, and lower estimates of fat intake, compared to the multiple 24HR variables.
Table 2
Table 2
Mean (S.D.) values for daily fruit, vegetable, and fat intake derived from multiple 24-hour food recalls, paper screener surveys, and computerized screeners
As shown in Table 3, deattenuated correlations between the multiple 24HR and paper and CD-ROM versions of the screeners were similar. To test for significance between CD-ROM and paper deattenuated correlations, r values were converted to Z scores, and the difference in Z scores was then divided by the pooled standard error. Using this method, the CD-ROM fruit intake estimate had a significantly stronger association with the multiple 24HR estimate than the paper fruit intake estimate (r=0.73 for CD-ROM vs 0.54 for paper; P<.05); other comparisons between CD-ROM and paper versions, however, were not significantly different.
Table 3
Table 3
Deattenuated correlations with multiple 24HR of paper and CD-ROM administrations of FVS fruit intake, FVS vegetable intake, FVS fruit and vegetable intake, and BFS fat intakea
Discussion
Accurate portrayal of dietary intake requires both frequency and portion size; frequency is the more important of the two (54,6265), but portion size estimation makes an important contribution (36). The computerized screeners developed in this preliminary investigation and tested in a sample of older, overweight adults focused on enhanced portion-size estimation, and produced correlations with multiple 24HR in line with previously published studies (4952,54).
Fruit and vegetable intake
The original performance evaluation of the paper version of the FVS (51), with 462 adults aged 20–70, reported correlations with the multiple 24HR of 0.66 for men and 0.51 for women, compared to 0.54 (CD-ROM) and 0.53 (paper) in the present study. Peterson and colleagues (52) (N=315) reported an overall correlation of 0.40 between the FVS and 24HR. Thompson et al (61) found that estimates of daily fruit and vegetable intake were within 1.2 servings per day of the 24HR estimates, while in the present study estimates were within on average 2.7–3.0 servings per day of the 24HR estimates. It is possible that lower estimates of fat intake might be because fewer questions are asked on the brief screener than the 24HR.
For estimated usual fruit and vegetable or fat intake, a complete FFQ or 24HR is recommended. According to Thompson and colleagues (51), the FVS and the much-longer FFQ performed similarly in terms of relative risk estimates, suggesting that the FVS might suffice in situations requiring brevity.
Fat intake
The computerized and paper assessments were about equally correlated with the multiple 24HR in measuring fat intake. Correlations were 0.55 (CD-ROM) and 0.50 (paper) with multiple 24HR fat intake, similar to the 0.58 correlation reported in Block et al (49) and higher than the 0.45 correlation reported in a previous worksite study (66). Both screener versions estimated less total fat, although the CD-ROM estimate was closer to the 24HR. Social desirability was not significantly related to the BFS. The discrepancy between both BFS versions compared to the multiple 24HR may be related to the concept of “good” vs “bad” foods (67). It is possible that users felt freer to report their intake of “bad” (ie, higher-fat) foods to a machine. Less complex BFS foods like eggs and hot dogs had higher agreement between the two screeners than more amorphous foods such as potato chips. The lower estimates of fat intake might be because fewer fat intake questions are asked on the brief screener than the multiple 24HR, and high correlations between the two modalities may not to be expected.
Limitations
The multiple 24HR is not devoid of error. The screeners measure a typical diet over 2 months and the multiple 24HR measures particular days. If large dietary changes occurred during the assessment period, agreement would be lowered. A limitation of the multiple 24HR (analyzed with the NDSR software) as a criterion for portion estimation is that it does not readily provide individual food serving size information for the identical foods on the screeners. Thus, comparisons with the multiple 24HR may not ensure “validity” of the computerized or paper screeners. Future studies might use methods that focus on the assessment of actual portion size consumed, such as observational feeding (30). The present sample was composed of mostly female overweight adults, many of whom were dieting to lose weight. The preponderance of females was expected, given the focus on diet and given the larger proportion of females in the general older adult population in the area (60.8% female over 65 years of age; 2000 U.S. Census).
Applications to General Practice
The study indicated that interactive computerized dietary screeners offer similar estimates to paper assessment and significantly correlate with multiple 24HR estimates for measuring fruit/vegetable and fat intake in overweight adults. Our findings suggest that the computerized version of the FVS and BFS is feasible for use in qualitative clinical or quantitative research situations where it is not possible to administer longer dietary assessments.
FUTURE RESEARCH
This study is one of very few focused specifically on increasing portion size estimation accuracy. Results are encouraging. The next step in this line of research is to identify and test more innovative ways to increase accuracy of portion estimation. Also, more formal validation is needed. Future research should vigorously test a revised version with other populations and with the use of “in vivo” observational portion-serving methods in addition to multiple 24HR, and incorporate a wider range of food serving sizes.
Footnotes
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Contributor Information
Deborah J. Toobert, Oregon Research Institute, 1715 Franklin Blvd., Eugene, OR 97403-1983, Phone: (541) 484-2123, Fax: (541) 434-1505.
Lisa A. Strycker, Oregon Research Institute, 1715 Franklin Blvd., Eugene, OR 97403-1983, Phone: (541) 484-2123, Fax: (541) 434-1505.
Sarah E. Hampson, Oregon Research Institute, 1715 Franklin Blvd., Eugene, OR 97403-1983, Phone: (541) 484-2123, Fax: (541) 484-1108.
Erika Westling, Oregon Research Institute, 1715 Franklin Blvd., Eugene, OR 97403-1983, Phone: (541) 484-2123, Fax: (541) 484-1108.
Steven M. Christiansen, InterVision Media, 261 E. 12thAvenue, Eugene, OR 97401, Phone: (541) 343-7993, Fax: (541) 345-5951.
Thomas G. Hurley, Diet Assessment Unit, Cancer Prevention and Control Program and Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, 915 Greene Street, Suite 241-2, Columbia, SC 29208, Phone: (803) 576-5621, Fax: (803) 576-5615.
James R. Hébert, Cancer Prevention and Control Program, Health Sciences Distinguished Professor, Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, 915 Greene Street, Suite 241-2, Columbia, SC 29208, Phone: (803) 576-5666, Fax: (803) 576-5615.
1. Rutishauser IHE. Food models, photographs or household measures? Proc Nutr Soc Aus. 1982;7:144–145.
2. Nelson M, Atkinson M, Darbyshire S. Food photography I: the perception of food portion size from photographs. Br J Nutr. 1994;72:649–663. [PubMed]
3. Godwin SL, Chambers E, Cleveland L. Accuracy of reporting dietary intake using various portion-size aids in-person and via telephone. J Am Diet Assoc. 2004;104:585–594. [PubMed]
4. Howat PM, Mohan R, Champagne C, Monlezun C, Wozniak P, Bray GA. Validity and reliability of reported dietary intake data. J Am Diet Assoc. 1994;94:169–173. [PubMed]
5. Lansky D, Brownell KD. Estimates of food quantity and calories: errors in self-report among obese patients. Am J Clin Nutr. 1982;35:727–732. [PubMed]
6. Rapp SA, Dubbert PM, Burkett PA, Buttross Y. Food portion size estimation by men with type II diabetes. J Am Diet Assoc. 1986;86:249–251. [PubMed]
7. Schatzkin A, Kipnis V, Carroll RJ, Midthune D, Subar AF, Bingham S, Schoeller DA, Troiano RP, Freedman LS. A comparison of a food frequency questionnaire with a 24HR recall for use in an epidemiological cohort study: results from the biomarker-based Observing Protein and Energy Nutrition (OPEN) study. Int J Epidemiol. 2003;32:1054–1062. [PubMed]
8. Subar AF, Kipnis V, Troiano RP, Midthune D, Schoeller DA, Bingham S, Sharbaugh CO, Trabulsi J, Runswick S, Ballard-Barbash R, Sunshine J, Schatzkin A. Using intake biomarkers to evaluate the extent of dietary misreporting in a large sample of adults: the OPEN study. Am J Epidemiol. 2003;158:1–13. [PubMed]
9. Schoeller DA, Schoeller DA. Validation of habitual energy intake. Public Health Nutr. 2002;5:883–888. [PubMed]
10. Scagliusi FB, Polacow VO, Artioli GG, Benatti FB, Lancha AH. Selective underreporting of energy intake in women: magnitude, determinants, and effect of training. J Am Diet Assoc. 2003;103:1306–1313. [PubMed]
11. Hebert JR, Ebbeling CB, Matthews CE, Hurley TG, Ma Y, Druker S, Clemow L. Systematic errors in middle-aged women’s estimates of energy intake: comparing three self-report measures to total energy expenditure from doubly labeled water. Ann Epidemiol. 2002;12:577–586. [PubMed]
12. Hebert JR, Hurley TG, Peterson KE, Resnicow K, Thompson FE, Yaroch AL, Ehlers M, Midthune D, Williams GC, Greene GW, Nebeling L. Social desirability trait influences on self-reported dietary measures among diverse participants in a multicenter multiple risk factor trial. J Nutr. 2008;138:226S–234S. [PubMed]
13. Hebert JR, Ma Y, Clemow L, Ockene IS, Saperia G, Stanek EJ, 3rd, Merriam PA, Ockene JK. Gender differences in social desirability and social approval bias in dietary self report. Am J Epidemiol. 1997;146:1046–1055. [PubMed]
14. Hébert JR, Peterson KE, Hurley TG, Stoddard AM, Cohen N, Field AE, Sorensen G. The effect of social desirability trait on self-reported dietary measures among multi-ethnic female health center employees. Ann Epidemiol. 2001;11:417–427. [PubMed]
15. Ueland O, Cardello AV, Merrill EP, Lesher LL. Effect of portion size information on food intake. J Am Diet Assoc. 2009;109:127. [PubMed]
16. Bandini LG, Schoeller DA, Cyr HN, Dietz WH. Validity of reported energy intake in obese and nonobese adolescents. Am J Clin Nutr. 1990;52:421–425. [PubMed]
17. Pedersen SD, Kang J, Kline GA. Portion control plate for weight loss in obese patients with type 2 diabetes mellitus: a controlled clinical trial. Arch Intern Med. 2007;167:1277–1283. [PubMed]
18. Camelon KM, Hådell K, Jämsén PT, Ketonen KJ, Kohtamäki HM, Mäkimatilla S, Törmälä M, Valve RH. For the DAIS Project Group. A visual method of teaching meal planning. J Am Diet Assoc. 1998;98:1155–1158. [PubMed]
19. Engell D, Kramer M, Zaring D, Birch L, Rolls BJ. Effects of serving size on food intake in children and adults. [Accessed June 30, 2010];Obes Res. 1995 3:S381. Abstract obtained from The Diet Plate. Portion control made easy Web site http://www.dietplate.us/. Published 2008.
20. Acheson KJ, Campbell IT, Edholm OG, Miller DS, Stock MJ. The measurement of food and energy intake in man—an evaluation of some techniques. Am J Clin Nutr. 1980;33:1147–1154. [PubMed]
21. Gittelsohn J, Shankar AV, Pokhrel RP, West KP. Accuracy of estimating food intake by observation. J Am Diet Assoc. 1994;94:1273–1277. [PubMed]
22. Goodwin RA, Brulé D, Junkins EA, Dubois S, Beer-Borst S. Development of a food and activity record and a portion-size model booklet for use by 6- to 17-year olds: a review of focus-group testing. J Am Diet Assoc. 2001;101:926–928. [PubMed]
23. Moore MC, Judlin BC, Kennimur P. Using graduated food models in taking dietary histories. J Am Diet Assoc. 1967;51:447–450. [PubMed]
24. Posner BM, Smigelski C, Duggal A, Morgan JL, Cobb J, Cupples LA. Validation of two-dimensional models for estimation of portion size in nutrition research. J Am Diet Assoc. 1992;92:738–741. [PubMed]
25. Chambers E, 4th, Godwin SL, Vecchio FA. Cognitive strategies for reporting portion sizes using dietary recall procedures. J Am Diet Assoc. 2000;100:891–897. [PubMed]
26. Godwin S, McGuire B, Chambers E, 4th, McDowell M, Cleveland L, Edwards-Perry E, Ingwersen L. Evaluation of portion size estimation aids used for meat in dietary surveys. Nutr Res. 2001;21:1217–1233.
27. Byrd-Bredbenner C, Schwartz J. The effect of practical portion size measurement aids on the accuracy of portion size estimates made by young adults. J Hum Nutr Diet. 2004;17:351–357. [PubMed]
28. Robson PJ, Livingstone MB. An evaluation of food photographs as a tool for quantifying food and nutrient intakes. Public Health Nutr. 2000;3:183–192. [PubMed]
29. Turconi G, Guarcello M, Berzolari FG, Carolei A, Bazzano R, Roggi C. An evaluation of a colour food photography atlas as a tool for quantifying food portion size in epidemiological dietary surveys. Eur J Clin Nutr. 2005;59:923–931. [PubMed]
30. Subar AF, Crafts J, Zimmermann TP, Wilson M, Mittl B, Islam NG, McNutt S, Potischman N, Buday R, Hull SG, Baranowski T, Guenther PM, Willis G, Tapia R, Thompson FE. Assessment of the accuracy of portion size reports using computer-based food photographs aids in the development of an automated self-administered 24HR recall. J Am Diet Assoc. 2010;110:55–64. [PubMed]
31. Beasley JM, Davis A, Riley WT. Evaluation of a web-based, pictorial diet history questionnaire. Public Health Nutr. 2009;12:651–659. [PMC free article] [PubMed]
32. Gines DJ, Parish F. Using computer graphics of life size foods to aid in estimation of serving sizes [Abstract] J Am Diet Assoc. 1989;89:A-19.
33. Ayala GX. An experimental evaluation of a group- versus computer-based intervention to improve food portion size estimation skills. Health Educ Res. 2006;21:133–145. [PubMed]
34. Carlton DJ, Kicklighter JR, Jonnalagadda SS, Shoffner MB. Design, development, and formative evaluation of “Put Nutrition into Practice,” a multimedia nutrition education program for adults. J Am Diet Assoc. 2000;100:555–563. [PubMed]
35. Riley WT, Beasley J, Sowell A, Behar A. Effects of a Web-based food portion training program on food portion estimation. J Nutr Educ Behav. 2007;39:70–76. [PMC free article] [PubMed]
36. Foster E, Matthews JNS, Marshall L, Mathers JC, Nelson M, Barton KL, Wrieden WL, Cornelissen P, Harris J, Adamson AJ. Children’s estimates of food portion size: the development and evaluation of three portion size assessment tools for use with children. Br J Nutr. 2008;99:175–184. [PubMed]
37. Nelson M, Atkinson M, Darbyshire S. Food photography II: use of food photographs for estimating portion size and nutrient content of meals. Br J Nutr. 1996;76:31–49. [PubMed]
38. Nelson M, Haraldsdottir J. Food photographs: practical guidelines I. design and analysis of studies to validate portion size estimates. Public Health Nutr. 1998;1:219–230. [PubMed]
39. Nelson M, Haraldsdottir J. Food photographs: practical guidelines II. development and use of photographic atlases for assessing food portion size. Public Health Nutr. 1998;1:231–237. [PubMed]
40. Zoellner J, Anderson J, Gould SM. Development and formative evaluation of a bilingual interactive multimedia dietary assessment tool. [Accessed June 30, 2010];Journal of Extension. 44:1–19. Available at. Web site http://www.joe.org/joe/2006february/a8.shtml. Published 2006.
41. Kohlmeier L. Future of dietary exposure assessment. Am J Clin Nutr. 1995;61:S702–S709. [PubMed]
42. Brug J, Campbell M, Van Assema P. The application and impact of computer-generated personalized nutrition education: a review of the literature. Patient Educ Couns. 1999;36:145–156. [PubMed]
43. Brug J, Oenema A, Campbell M. Past, present, and future of computer-tailored nutrition education. Am J Clin Nutr. 2003;77:1028S–1034S. [PubMed]
44. Campbell M, Honess-Morreale L, Farrell D, Carbone E, Brasure M. A tailored multimedia nutrition education pilot program for low-income women receiving food assistance. Health Educ Res. 1999;14:257–267. [PubMed]
45. Gould S, Anderson J. Using interactive multimedia nutrition education to reach low-income persons: an effectiveness evaluation. J Nutr Educ. 2000;32:204–213.
46. Jantz C, Anderson J, Gould S. Using computer-based assessments to evaluate interactive multimedia nutrition education among low-income predominantly Hispanic participants. J Nutr Educ Behav. 2002;34:252–260. [PubMed]
47. Hébert JR, Hurley TG, Chiraboga DE, Barone J. A comparison of selected nutrient intakes derived from three diet assessment methods used in a low-fat maintenance trial. Public Health Nutr. 1998;1:207–14. [PubMed]
48. Hébert JR, Ebbeling CB, Matthews CE, Ma Y, Clemow L, Hurley TG, Druker S. Systematic errors in middle-aged women’s estimates of energy intake: comparing three self-report measures to total energy expenditure from doubly labeled water. Ann Epidemiol. 2002;12:577–586. [PubMed]
49. Block G, Clifford C, Naughton MD, Henderson M, McAdams M. A brief dietary screen for high fat intake. J Nutr Educ. 1989;21:199–207.
50. Thompson FE, Subar AF, Smith AF, Midthune D, Radimer KL, Kahle LL, Kipnis V. Fruit and vegetable assessment: performance of 2 new short instruments and a food frequency questionnaire. J Amer Diet Assoc. 2002;102:1764–1772. [PubMed]
51. Thompson FE, Kipnis V, Subar AF, Krebs-Smith SM, Kahle LL, Midthune D, Potischman N, Schatzkin A. Evaluation of 2 brief instruments and a food-frequency questionnaire to estimate daily number of servings of fruit and vegetables. Am J Clin Nutr. 2000;71:1503–1510. [PubMed]
52. Peterson KE, Hebert JR, Hurley TG, Resnicow K, Thompson FE, Greene GW, Shaikh AR, Yaroch AL, Williams GC, Salkeld J, Toobert DJ, Domas A, Elliot DL, Hardin J, Nebeling L. Accuracy and precision of two short screeners to assess change in fruit and vegetable consumption among diverse populations participating in health promotion intervention trials. J Nutr. 2008;138:218S–225S. [PubMed]
53. Thompson FE, Byers T. Dietary assessment resource manual. J Nutr. 1994;124:2245S–2317S. [PubMed]
54. Greene GW, Resnicow K, Thompson FE, Peterson KE, Hurley TG, Hebert JR, Toobert DJ, Williams G, Elliot DL, Sher TG, Domas A, Nebeling L, Midthune D, Stacewicz-Sapuntzakis M, Yaroch AL. Correspondence of the NCI Fruit and Vegetable Screener to repeat 24-h recalls and serum carotenoids in behavioral intervention trials. J Nutr. 2008;138:200S–204S. [PubMed]
55. Block G, Hartman AM, Dresser CM, Carroll MD, Gannon J, Gardner L. A data-based approach to diet questionnaire design and testing. Am J Epidemiol. 1986;124:453–469. [PubMed]
56. United States Department of Agriculture. Center for Nutrition Policy and Promotion. The Food Guide Pyramid. Home and Garden Bulletin No. 252. Washington, DC: 1996.
57. John OP, Srivastava S. The Big Five trait taxonomy: history, measurement, and theoretical perspectives. In: Pervin LA, John OP, editors. Handbook of Personality: Theory and Research. 2. New York: Guilford; 1999. pp. 102–138.
58. Block AF, Gillespie C, Rosenbaum EH, Jenson C. A rapid food screener to assess fat and fruit and vegetable intake. Am J Prev Med. 2000;18:284–288. [PubMed]
59. Paulhus DL. Self-deception and impression management in test responses. In: Angleiter A, Wiggins JS, editors. Personality Assessment via Questionnaire: Current Issues in Theory and Measurement. Berlin, Germany: Springer-Verlag; 1986. pp. 17–41.
60. Chew LD, Bradley KA, Boyko EJ. Brief questions to identify patients with inadequate health literacy. Fam Med. 2004;36:588–594. [PubMed]
61. Freedman LS, Carroll RJ, Wax Y. Estimating the relation between dietary intake obtained from a food frequency questionnaire and true average intake. Am J Epidemiol. 1991;134:310–320. [PubMed]
62. Clapp JA, McPherson RS, Reed DB, Hsi BP. Comparison of a food frequency questionnaire using reported vs standard portion sizes for classifying individuals according to nutrient intake. J Am Diet Assoc. 1991;91:316–320. [PubMed]
63. Noethlings U, Hoffmann K, Bergmann MM, Boeing H. Portion size adds limited information on variance in food intake of participants in the EPIC-Potsdam study. J Nutr. 2003;133:510–515. [PubMed]
64. Peterson KE, Hebert JR, Hurley TG, Resnicow K, Thompson FE, Greene GW, Shaikh AR, Yaroch AL, Williams GC, Salkeld J, Toobert DJ, Domas A, Elliot DL, Hardin J, Nebeling L. Accuracy and precision of two short screeners to assess change in fruit and vegetable consumption among diverse populations participating in health promotion intervention trials. J Nutr. 2008;138:218S–225S. [PubMed]
65. Subar A, Thompson F, Smith A, Jobe J, Ziegler R, Potischman N, Schatzkin A, Hartman A, Swanson C, Kruse L. Improving food frequency questionnaires: a qualitative approach using cognitive interviewing. J Amer Diet Assoc. 1995;95:781–788. [PubMed]
66. Glasgow RE, Perry JD, Toobert DJ, Hollis JF. Brief assessments of dietary behavior in field settings. Addict Behav. 1996;2:239–247. [PubMed]
67. Vuckovic N, Ritenbaugh C, Taren DL, Tober M. A qualitative study of participant’s experiences with dietary assessment. J Am Diet Assoc. 2002;102:1764–1772. [PubMed]